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Here is a issue I discovered in pandas version '0.12.0' (already present in preview version I think)
Issue:
When appending a DataFrame (with a new datetime64 column) to an existing one, the default value for missing values should be a pandas.tslib.NaT.
Could be nice if, be default, when a new "datetime64" column is added, the default for missing values is NaT. Otherwise this creates problem when, for example, saving as HDF5 using pytable which does not accept mixed types per column.
Have a nice day,
Patrick
The text was updated successfully, but these errors were encountered:
Hello,
Here is a issue I discovered in pandas version '0.12.0' (already present in preview version I think)
Issue:
When appending a DataFrame (with a new datetime64 column) to an existing one, the default value for missing values should be a pandas.tslib.NaT.
Example:
While in reallity we want:

A simple work around is:
Could be nice if, be default, when a new "datetime64" column is added, the default for missing values is NaT. Otherwise this creates problem when, for example, saving as HDF5 using pytable which does not accept mixed types per column.
Have a nice day,
Patrick
The text was updated successfully, but these errors were encountered: